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International Conference on Network Applications Protocols and Services (NETAPPS)

作者： Noraini binti Mohd Banua Fathey Mohammed Moaadh Mukred Mahmood A. Bazel Mohd Samsu Bin Sajat Awang Had Salleh Graduate School School of Computing University Utara Malaysia Jitra Kedah Sunway Business School Sunway University Subang Jaya Selangor Malaysia Faculty of Engineering and Computing University of Science and Technology Taiz City Yemen School of Computing Universiti Utara Malaysia (UUM) Sintok Kedah Malaysia

ISBN: (数字)9798331519513

ISBN: (纸本)9798331519520

Gamification is a technique that uses game mechanics and aspects to engage and inspire people to complete a task. Gamification has grown in popularity in several industries recently, including software development. The purpose of this systematic literature review (SLR) is to review the most recent findings in the field of gamification's application to agile methodology between the years 2015 and 2024. With the help of search terms like “gamification,” “agile methodology,” and “game mechanics,” “agile development,” a thorough scan of databases including the IEEE Xplore, and Scopus was carried out. Articles and studies released in English were examined. Data were extracted analysed using a spreadsheet to organize information such as study design, sample size, research setting, kinds of game mechanics used, and major findings. To combine the results from the chosen studies and discover any recurring themes or patterns, a thematic analysis method was used. The findings of this SRL points to areas for further study and contributes to understanding the benefits and best practices of using gamification in agile methodology and provides insights for practitioners and researchers in the field.

关键词： Industries Protocols Databases Games Teamwork Internet Data mining Best practices Systematic literature review Software development management

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Experimental Evaluation of a Checklist-Based Inspection Technique to Verify the Compliance of Software Systems with the Brazilian General Data Protection Law

arXiv

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arXiv 2023年

作者： Cerqueira, Diego André de Mello, Rafael Maiani Travassos, Guilherme Horta Systems Engineering and Computer Science Graduate Program Federal University of Rio de Janeiro Brazil Computing Institute Federal University of Rio de Janeiro Brazil

Background/Context: Recent laws to ensure the security and protection of personal data establish new software requirements. Consequently, new technologies are needed to guarantee software quality under the perception of privacy and protection of personal data. Therefore, we created a checklist-based inspection technique (LGPDCheck) to support the identification of defects in software artifacts based on the principles established by the Brazilian General Data Protection Law (LGPD). Objective/Aim: To evaluate the effectiveness and efficiency of LGPDCheck for verifying privacy and data protection (PDP) in software artifacts compared to ad-hoc techniques. Method: To assess LGPDCheck and ad-hoc techniques experimentally through a quasi-experiment (two factors, five treatments). The data will be collected from IoT-based health software systems built by software engineering students from the Federal University of Rio de Janeiro. The data analyses will compare results from ad-hoc and LGPDCheck inspections, the participant's effectiveness and efficiency in each trial, defects' variance and standard deviation, and time spent with the reviews. The data will be screened for outliers, and normality and homoscedasticity will be verified using the Shapiro-Wilk and Levene tests. Nonparametric or parametric tests, such as the Wilcoxon or Student's t-tests, will be applied as appropriate. © 2023, CC BY-NC-ND.

关键词： Data privacy

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Scaling Neural-Network-Based Molecular Dynamics with Long-Range Electrostatic Interactions to 51 Nanoseconds per Day

arXiv

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arXiv 2025年

作者： Li, Jianxiong Zhang, Beining Li, Mingzhen Hu, Siyu Zeng, Jinzhe Liu, Lijun Yuan, Guojun Wang, Zhan Tan, Guangming Jia, Weile SKLP Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence and Data Science University of Science and Technology of China Suzhou Big Data & AI Research and Engineering Center Hefei China Department of Mechanical Engineering Graduate School of Engineering The University of Osaka Suita Japan

Neural network-based molecular dynamics (NNMD) simulations incorporating long-range electrostatic interactions have significantly extended the applicability to heterogeneous and ionic systems, enabling effective modeling critical physical phenomena such as protein folding and dipolar surface and maintaining ab initio accuracy. However, neural network inference and long-range force computation remain the major bottlenecks, severely limiting simulation speed. In this paper, we target DPLR, a state-of-the-art NNMD package that supports long-range electrostatics, and propose a set of comprehensive optimizations to enhance computational efficiency. We introduce (1) a hardware-offloaded FFT method to reduce the communication overhead;(2) an overlapping strategy that hides long-range force computations using a single core per node, and (3) a ring-based load balancing method that enables atom-level task evenly redistribution with minimal communication overhead. Experimental results on the Fugaku supercomputer show that our work achieves a 37x performance improvement, reaching a maximum simulation speed of 51 ns/*** Codes 82M37 Copyright © 2025, The Authors. All rights reserved.

关键词： Supercomputers

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Gauge-independent transition separating confinement and Higgs phases in lattice SU(2) gauge theory with a scalar field in the fundamental representation

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Physical Review D 2024年第5期109卷 054505-054505页

作者： Ryu Ikeda Seikou Kato Kei-Ichi Kondo Akihiro Shibata Department of Physics Graduate School of Science and Engineering Chiba University Chiba 263-8522 Japan Oyama National College of Technology Oyama 323-0806 Japan Department of Physics Graduate School of Science Chiba University Chiba 263-8522 Japan Computing Research Center High Energy Accelerator Research Organization (KEK) Tsukuba 305-0801 Japan Department of Accelerator Science SOKENDAI (The Graduate University for Advanced Studies) Tsukuba 305-0801 Japan

According to the preceding studies, the lattice SU(2) gauge-scalar model with a single scalar field in the fundamental representation of the gauge group has a single confinement-Higgs phase where confinement and Higgs regions are subregions of an analytically continued single phase and there are no thermodynamic phase transitions, which is a well-known consequence of the Osterwalder-Seiler-Fradkin-Shenker theorem. In this paper, we show that we can define new types of gauge-invariant operators by combining the original fundamental scalar field and the so-called color-direction field which is obtained by change of field variables based on the gauge-covariant decomposition of the gauge field due to Cho-Duan-Ge-Shabanov and Faddeev-Niemi. By performing the numerical simulations on the lattice without any gauge fixing, we reproduce the conventional thermodynamic transition line in the weak gauge coupling, and moreover we find a new transition line detected by the new gauge-invariant operators which separates the confinement-Higgs phase into two parts, confinement phase and the Higgs phase, in the strong gauge coupling. All results are obtained in the gauge-independent way, since no gauge fixing has been imposed in the numerical simulations. Moreover, we discuss a physical interpretation for the new transition from the viewpoint of the realization of a global symmetry.

关键词： Lattice field theory

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Organic-inorganic hybrid piezotronic bipolar junction transistor for pressure sensing

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Microsystems & Nanoengineering 2024年第3期10卷 229-244页

作者： Emad Iranmanesh Zihao Liang Weiwei Li Congwei Liao Shunyu Jin Chuan Liu Kai Wang Shengdong Zhang Charalampos Doumanidis Gehan A.J.Amaratunga Hang Zhou Guangdong Provincial Key Laboratory of In-Memory Computing Chips School of Electronic and Computer EngineeringPeking University Shenzhen Graduate SchoolShenzhen 518055P.R.China School of Mechanical Engineering Guangdong Technion-Israel Institute of TechnologyShantou 515063P.R.China State Key Laboratory of Microelectronics Device and Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029P.R.China Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of ChinaHefei 23000PR China School of Electronics and Information Technology Sun Yat-sen UniversityNo.132 East Waihuan RoadGuangzhou 510006P.R.China Department of Mechanical Aerospaceand Biomedical EngineeringUniversity of South AlabamaShelby Hall3128MobileAL 36688USA Electrical Engineering Division Department of EngineeringUniversity of Cambridge9 JJ Thomson AvenueCambridge CB30FAUnited Kingdom Zhejiang University International CampusHainingChina

With the rapid development of the Internet of Things(IoTs),wearable sensors are playing an increasingly important role in daily monitoring of personal health and *** signal-to-noise-ratio has become the most critical performance factor to *** enhance it,on the one hand,good sensing materials/devices have been employed;on the other hand,signal amplification and noise reduction circuits have been ***,most of these devices and circuits work in an active sampling mode,requiring frequent data acquisition and hence,entailing high-power *** this scenario,a flexible and wearable event-triggered sensor with embedded signal amplification without an external power supply is of great ***,we report a flexible two-terminal piezotronic n-p-n bipolar junction transistor(PBJT)that acts as an autonomous and highly sensitive,current-and/or voltage-mediated pressure *** PBJT is formed by two back-to-back piezotronic diodes which are defined as emitter-base and collectorbase *** force exertion on the emitter side,as a result of the piezoelectric effect,the emitter-base diode is forward biased while the collector-base diode is reverse *** to the inherent BJT amplification effect,the PBJT achieves record-high sensitivities of 139.7 kPa^(-1)(current-based)and 88.66 kPa^(-1)(voltage-based)in sensing *** PBJT also has a fast response time of<110 ms under exertion of dynamic stimuli ranging from a flying butterfly to a gentle finger ***,the PBJT advances the state of the art not only in terms of sensitivity but also in regard to being self-driven and autonomous,making it promising for pressure sensing and other IoT applications.

关键词： bipolar transistor IoT

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Tight Analysis of Convex Splitting with Applications in Quantum Information Theory

Tight Analysis of Convex Splitting with Applications in Quan...

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Cheng, Hao-Chung Gao, Li Department of Electrical Engineering Graduate Institute of Communication Engineering Taipei106 Taiwan Department of Mathematics National Taiwan University Taipei106 Taiwan Center for Quantum Science and Engineering National Taiwan University Taiwan Quantum Computing Center New Taipei City236 Taiwan Physics Division National Center for Theoretical Sciences Taipei10617 Taiwan University of Houston Department of Mathematics HoustonTX77204 United States

ISBN: (纸本)9781665475549

Convex splitting is a powerful technique in quantum information theory that applies to proving the achievability of numerous information-processing protocols. In this paper, we establish a one-shot error exponent and a one-shot strong converse for convex splitting with trace distance as the error criterion. Our result exhibits the following features. Firstly, the derived error exponent is given in terms of a generalized sandwiched Rényi mutual information which is additive for product states. Hence, our bound directly applies to the n-fold product scenario for any blocklength. Secondly, the one-shot error bound is meaningful in the sense that the error exponent is all positive on the achievable rate region. This leads to new one-shot exponent results in various tasks in quantum information theory. Conversely, we prove a one-shot strong converse to demonstrate that the convex splitting error converges to one exponentially fast at rates outside the achievable rate region. We also establish an optimal one-shot characterization of the sample complexity for convex splitting, which yields matched second-order asymptotics, showing the tightness of our result. This then leads to a stronger one-shot analysis in quantum information *** technique is to introduce a unified functional analytic approach using Kosaki's noncommutative weighted Lp norm. Our results then follow from tight analysis based on such a norm *** full version of the manuscript can be found at [arXiv:2304.12055] [1]. © 2023 IEEE.

关键词： Errors

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Nearly optimal quasienergy estimation and eigenstate preparation of time-periodic Hamiltonians by Sambe space formalism

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Physical Review Research 2025年第1期7卷 013068-013068页

作者： Kaoru Mizuta Department of Applied Physics Graduate School of Engineering The University of Tokyo Hongo 7-3-1 Bunkyo Tokyo 113-8656 Japan Photon Science Center Graduate School of Engineering The University of Tokyo Hongo 7-3-1 Bunkyo Tokyo 113-8656 Japan and RIKEN Center for Quantum Computing (RQC) Hirosawa 2-1 Wako Saitama 351-0198 Japan

Time-periodic (Floquet) systems are one of the most interesting nonequilibrium systems. As the computation of energy eigenvalues and eigenstates of time-independent Hamiltonians is a central problem in both classical and quantum computation, quasienergy and Floquet eigenstates are the important targets. However, their computation has difficulty of time dependence; the problem can be mapped to a time-independent eigenvalue problem by the Sambe space formalism, but it instead requires additional infinite-dimensional space and seems to yield higher computational cost than the time-independent cases. It is still unclear whether they can be computed with guaranteed accuracy as efficiently as the time-independent cases. We address this issue by rigorously deriving the cutoff of the Sambe space to achieve the desired accuracy and organizing quantum algorithms for computing quasienergy and Floquet eigenstates based on the cutoff. The quantum algorithms return quasienergy and Floquet eigenstates with guaranteed accuracy like quantum phase estimation, which is the optimal algorithm for outputting energy eigenvalues and eigenstates of time-independent Hamiltonians. While the time periodicity provides the additional dimension for the Sambe space and defines two different kinds of eigenstates, the query complexity of the algorithms achieves the near-optimal scaling in allowable errors. In addition, as a by-product of these algorithms, we also organize a quantum algorithm for Floquet eigenstate preparation, in which a preferred gapped Floquet eigenstate can be deterministically implemented with nearly optimal query complexity in the gap. These results show that, despite the difficulty of time dependence, quasienergy and Floquet eigenstates can be computed almost as efficiently as time-independent cases, shedding light on the accurate and fast simulation of nonequilibrium systems on quantum computers.

关键词： Quantum computers

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StegoFL: Using Steganography and Federated Learning to Transmit Malware

StegoFL: Using Steganography and Federated Learning to Trans...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Rong Wang Junchuan Liang Haiting Jiang Chaosheng Feng Chin-Chen Chang College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu China Graduate School of Science and Engineering Hosei University Tokyo Japan Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Federated learning (FL) has gained increasing popularity in today’s privacy-focused world due to its ability to break data silos. Although federated learning can keep all the training data private at each site, it results in clients not being effectively monitored, which may pose security risks to the learning process. Previous studies mainly focus on backdoor and poisoning attacks against federated learning. In this paper, we propose a new stegomalware attack in FL, where an attacker, disguised as a benign client, hides malware into his local model and distributes the malware to other clients through the FL process. Conducting such an attack faces two challenges. First, embedding data into a local model may degrade its model performance, making it easier for a server in FL to detect this anomaly. Second, the local model with embedded malware is recalculated after model aggregation, which unavoidably alters the malware’s carrier and hinders the success of malware extraction. To address these challenges, we propose a method called StegoFL to incorporate steganography into federated learning to transmit malware. Specifically, we split the malware to be transmitted into segments and randomly select a few model parameters as carriers. Each malware segment is concealed within these carriers over several training rounds. We also propose a simple value-mapping method to extract the embedded data by comparing the aggregated carrier values with a threshold. Experimental results demonstrate that StegoFL can circumvent server-side detection mechanisms, i.e., accuracy tests and parametric distribution comparisons. In addition, it increases transmission capacity by at least 50 times compared to state-of-the-art covert communication methods in federated learning.

关键词： Training Steganography Federated learning Training data Malware Data models Security Servers Monitoring Faces

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Real-Time Diagnosis of Configuration Errors for Software of AI Server Infrastructure

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IEEE Transactions on Dependable and Secure computing 2023年 1-12页

作者： Xu, Guangquan Ding, Xinru Xu, Sihan Jia, Yan Liu, Shaoying Feng, Shicheng Zheng, Xi Big Data School Qingdao Huanghai University Qingdao China College of Intelligence and Computing Tianjin University Tianjin China College of Cyber Science Nankai University Tianjin China Graduate School of Advanced Science and Engineering Hiroshima University Higashihiroshima Japan Department of Computing Macquarie University Sydney NSW Australia

Artificial intelligence (AI) server infrastructure has been built to support AI applications and handle data-intensive workloads. AI server infrastructure is the essential building blocks, and errors in AI server infrastructure may lead to fatal consequences to any AI applications built upon it. Compared to traditional software, software for AI server infrastructure is more configurable, and thus more likely to have configuration errors that might prevent correct software behaviors. Previous work on misconfiguration diagnosis requires sufficient execution history or manual intervention, and can hardly diagnose potential misconfigurations which are not triggered at launching. In this paper, we propose a real-time method to address these issues. Specifically, we combine program analysis and real-time log parsing to diagnose configuration errors. It maps each configuration option to the log code by applying program slicing only once, and parses real-time logs during the operation of the AI server without manual intervention. We evaluate the effectiveness of our approach on the core components of Hadoop, an exemplar AI Server Infrastructure Software. The results show that our method mapped more than 80% of the configuration options to log outputs, identified 90% of the configuration read sites as the slicing seeds, and successfully diagnosed about 10% configuration errors that can not be addressed by previous studies. IEEE

关键词： Real time systems

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MedCare4Home: A Mobile Medical System for Families

MedCare4Home: A Mobile Medical System for Families

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Mengting Wang Yen Le Chen-Hsiang Yu School of Computing and Data Science Wentworth Institute of Technology Boston MA U.S.A Multidisciplinary Graduate Engineering College of Engineering Northeastern University Boston MA U.S.A

Managing healthcare data is essential for everyone, but it can be challenging and time-consuming. Although several Web and mobile applications existed for personal medical record management, there were few home-based, family-oriented solutions. In this research, we proposed to design an easy-to-use solution for a family. We designed and implemented an innovative Home Medical Care System, MedCare4Home, by deploying a MERN stack Web application to a minicomputer, Raspberry Pi 4, and enabling a local network to connect different devices at home. The users can access the Web application using either a desktop, tablet, mobile phone, or simply a 7-inch LCD touchscreen. MedCare4Home not only allows users to keep track of appointments and organize healthcare documents, but it can also help set up medication reminders and provide a self-report interface for symptom collection for family members easily and securely in one place. The final developed system demonstrated that we achieved the original goal and beyond. We expect to extend our current hardware design and software development to support physiological data collection with external sensors in the future.

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